import cv2
import numpy as np
from skimage import data
from skimage.io import imread
import matplotlib.pyplot as plt
%matplotlib inline
#image = data.retina()
#image = data.astronaut()
image = imread(fname="rei.jpeg")
print(image.shape)
plt.imshow(image)
(1170, 1560, 3)
<matplotlib.image.AxesImage at 0x206d75ed520>
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# defining the range of masking
blue1 = np.array([110, 50, 50])
blue2 = np.array([130, 255, 255])
# initializing the mask to be
# convoluted over input image
mask = cv2.inRange(hsv, blue1, blue2)
# passing the bitwise_and over
# each pixel convoluted
res = cv2.bitwise_and(image, image, mask = mask)
# defining the kernel i.e. Structuring element
kernel = np.ones((5, 5), np.uint8)
# defining the opening function
# over the image and structuring element
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
fig, axes = plt.subplots(1, 2, figsize=(12, 12))
ax = axes.ravel()
ax[0].imshow(mask)
ax[0].set_title("Citra Input 1")
ax[1].imshow(opening, cmap='gray')
ax[1].set_title('Citra Input 2')
Text(0.5, 1.0, 'Citra Input 2')
# return video from the first webcam on your computer.
screenRead = cv2.VideoCapture(0)
# loop runs if capturing has been initialized.
while(1):
# reads frames from a camera
_, image = screenRead.read()
# Converts to HSV color space, OCV reads colors as BGR
# frame is converted to hsv
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# defining the range of masking
blue1 = np.array([110, 50, 50])
blue2 = np.array([130, 255, 255])
# initializing the mask to be
# convoluted over input image
mask = cv2.inRange(hsv, blue1, blue2)
# passing the bitwise_and over
# each pixel convoluted
res = cv2.bitwise_and(image, image, mask = mask)
# defining the kernel i.e. Structuring element
kernel = np.ones((5, 5), np.uint8)
# defining the opening function
# over the image and structuring element
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
# The mask and opening operation
# is shown in the window
cv2.imshow('Mask', mask)
cv2.imshow('Opening', opening)
# Wait for 'a' key to stop the program
if cv2.waitKey(1) & 0xFF == ord('a'):
break
# De-allocate any associated memory usage
cv2.destroyAllWindows()
# Close the window / Release webcam
screenRead.release()
import cv2
import numpy as np
from skimage import data
from skimage.io import imread
import matplotlib.pyplot as plt
%matplotlib inline
#image = data.retina()
#image = data.astronaut()
image = imread(fname="rei.jpeg")
print(image.shape)
plt.imshow(image)
(1170, 1560, 3)
<matplotlib.image.AxesImage at 0x16f9ff264f0>
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# defining the range of masking
blue1 = np.array([110, 50, 50])
blue2 = np.array([130, 255, 255])
# initializing the mask to be
# convoluted over input image
mask = cv2.inRange(hsv, blue1, blue2)
# passing the bitwise_and over
# each pixel convoluted
res = cv2.bitwise_and(image, image, mask = mask)
# defining the kernel i.e. Structuring element
kernel = np.ones((5, 5), np.uint8)
# defining the closing function
# over the image and structuring element
closing = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
fig, axes = plt.subplots(1, 2, figsize=(12, 12))
ax = axes.ravel()
ax[0].imshow(mask)
ax[0].set_title("Citra Input 1")
ax[1].imshow(closing, cmap='gray')
ax[1].set_title('Citra Input 2')
Text(0.5, 1.0, 'Citra Input 2')
# Python programe to illustrate
# Closing morphological operation
# on an image
# organizing imports
import cv2
import numpy as np
# return video from the first webcam on your computer.
screenRead = cv2.VideoCapture(0)
# loop runs if capturing has been initialized.
while(1):
# reads frames from a camera
_, image = screenRead.read()
# Converts to HSV color space, OCV reads colors as BGR
# frame is converted to hsv
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# defining the range of masking
blue1 = np.array([110, 50, 50])
blue2 = np.array([130, 255, 255])
# initializing the mask to be
# convoluted over input image
mask = cv2.inRange(hsv, blue1, blue2)
# passing the bitwise_and over
# each pixel convoluted
res = cv2.bitwise_and(image, image, mask = mask)
# defining the kernel i.e. Structuring element
kernel = np.ones((5, 5), np.uint8)
# defining the closing function
# over the image and structuring element
closing = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
# The mask and closing operation
# is shown in the window
cv2.imshow('Mask', mask)
cv2.imshow('Closing', closing)
# Wait for 'a' key to stop the program
if cv2.waitKey(1) & 0xFF == ord('a'):
break
# De-allocate any associated memory usage
cv2.destroyAllWindows()
# Close the window / Release webcam
screenRead.release()
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) Cell In[5], line 27 23 blue2 = np.array([130, 255, 255]) 25 # initializing the mask to be 26 # convoluted over input image ---> 27 mask = cv2.inRange(hsv, blue1, blue2) 29 # passing the bitwise_and over 30 # each pixel convoluted 31 res = cv2.bitwise_and(image, image, mask = mask) KeyboardInterrupt:
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Reading the input image
img = cv2.imread('rei.jpeg', 0)
# Taking a matrix of size 5 as the kernel
kernel = np.ones((5,5), np.uint8)
img_erosion = cv2.erode(img, kernel, iterations=1)
img_dilation = cv2.dilate(img, kernel, iterations=1)
fig, axes = plt.subplots(3, 2, figsize=(20, 20))
ax = axes.ravel()
ax[0].imshow(img, cmap = 'gray')
ax[0].set_title("Citra Input")
ax[1].hist(img.ravel(), bins = 256)
ax[1].set_title("Histogram Citra Input")
ax[2].imshow(img_erosion, cmap = 'gray')
ax[2].set_title("Citra Output Erosi")
ax[3].hist(img_erosion.ravel(), bins = 256)
ax[3].set_title("Histogram Citra Output Erosi")
ax[4].imshow(img_dilation, cmap = 'gray')
ax[4].set_title("Citra Output Dilasi")
ax[5].hist(img_dilation.ravel(), bins = 256)
ax[5].set_title("Histogram Citra Output Erosi")
Text(0.5, 1.0, 'Histogram Citra Output Erosi')
import cv2
import numpy as np
from skimage import data
from skimage.io import imread
import matplotlib.pyplot as plt
%matplotlib inline
#image = data.retina()
#image = data.astronaut()
image = imread(fname="rei.jpeg")
print(image.shape)
plt.imshow(image)
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# defining the range of masking
blue1 = np.array([110, 50, 50])
blue2 = np.array([130, 255, 255])
# initializing the mask to be
# convoluted over input image
mask = cv2.inRange(hsv, blue1, blue2)
# passing the bitwise_and over
# each pixel convoluted
res = cv2.bitwise_and(image, image, mask = mask)
# defining the kernel i.e. Structuring element
kernel = np.ones((5, 5), np.uint8)
# defining the closing function
# over the image and structuring element
closing = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
(1170, 1560, 3)
fig, axes = plt.subplots(1, 2, figsize=(12, 12))
ax = axes.ravel()
ax[0].imshow(mask)
ax[0].set_title("Citra Input 1")
ax[1].imshow(closing, cmap='gray')
ax[1].set_title('Citra Input 2')
Text(0.5, 1.0, 'Citra Input 2')
# Python programe to illustrate
# Gradient morphological operation
# on input frames
# organizing imports
import cv2
import numpy as np
# return video from the first webcam on your computer.
screenRead = cv2.VideoCapture(0)
# loop runs if capturing has been initialized.
while(1):
# reads frames from a camera
_, image = screenRead.read()
# Converts to HSV color space, OCV reads colors as BGR
# frame is converted to hsv
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# defining the range of masking
blue1 = np.array([110, 50, 50])
blue2 = np.array([130, 255, 255])
# initializing the mask to be
# convoluted over input image
mask = cv2.inRange(hsv, blue1, blue2)
# passing the bitwise_and over
# each pixel convoluted
res = cv2.bitwise_and(image, image, mask = mask)
# defining the kernel i.e. Structuring element
kernel = np.ones((5, 5), np.uint8)
# defining the gradient function
# over the image and structuring element
gradient = cv2.morphologyEx(mask, cv2.MORPH_GRADIENT, kernel)
# The mask and closing operation
# is shown in the window
cv2.imshow('Gradient', gradient)
# Wait for 'a' key to stop the program
if cv2.waitKey(1) & 0xFF == ord('a'):
break
# De-allocate any associated memory usage
cv2.destroyAllWindows()
# Close the window / Release webcam
screenRead.release()
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) Cell In[9], line 19 15 _, image = screenRead.read() 17 # Converts to HSV color space, OCV reads colors as BGR 18 # frame is converted to hsv ---> 19 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) 21 # defining the range of masking 22 blue1 = np.array([110, 50, 50]) KeyboardInterrupt: